Train_ML_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "672c685f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.16.1\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2748b208",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keras provides a handy API to download the MNIST dataset, and split them into\n",
    "# \"train\" dataset and \"test\" dataset.\n",
    "mnist = keras.datasets.mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c113571",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixels are normalized\n"
     ]
    }
   ],
   "source": [
    "# Normalize the input image so that each pixel value is between 0 to 1.\n",
    "train_images = train_images / 255.0\n",
    "test_images = test_images / 255.0\n",
    "print('Pixels are normalized')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "43e767ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the first 25 images in the training dataset.\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "  plt.subplot(5,5,i+1)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  plt.grid(False)\n",
    "  plt.imshow(train_images[i], cmap=plt.cm.gray)\n",
    "  plt.xlabel(train_labels[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ffa57a2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\program\\anaconda3\\envs\\test\\lib\\site-packages\\keras\\src\\layers\\core\\input_layer.py:25: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.9101 - loss: 0.2970\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.9833 - loss: 0.0537\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.9897 - loss: 0.0336\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.9899 - loss: 0.0303\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.9929 - loss: 0.0216\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x250d5b3e670>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the model architecture\n",
    "model = keras.Sequential([\n",
    "  keras.layers.InputLayer(input_shape=(28, 28)),\n",
    "  keras.layers.Reshape(target_shape=(28, 28, 1)),\n",
    "  keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu),\n",
    "  keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=tf.nn.relu),\n",
    "  keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "  keras.layers.Dropout(0.25),\n",
    "  keras.layers.Flatten(),\n",
    "  keras.layers.Dense(10)\n",
    "])\n",
    "\n",
    "# Define how to train the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the digit classification model\n",
    "model.fit(train_images, train_labels, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "45bf1b0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ reshape (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9216</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">92,170</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ reshape (\u001b[38;5;33mReshape\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9216\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │          \u001b[38;5;34m92,170\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">332,960</span> (1.27 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m332,960\u001b[0m (1.27 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">110,986</span> (433.54 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m110,986\u001b[0m (433.54 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">221,974</span> (867.09 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m221,974\u001b[0m (867.09 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9e91438e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9843 - loss: 0.0508\n",
      "Test accuracy: 0.9872000217437744\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using all images in the test dataset.\n",
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a504466f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1800 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A helper function that returns 'red'/'black' depending on if its two input\n",
    "# parameter matches or not.\n",
    "def get_label_color(val1, val2):\n",
    "  if val1 == val2:\n",
    "    return 'black'\n",
    "  else:\n",
    "    return 'red'\n",
    "\n",
    "# Predict the labels of digit images in our test dataset.\n",
    "predictions = model.predict(test_images)\n",
    "\n",
    "# As the model output 10 float representing the probability of the input image\n",
    "# being a digit from 0 to 9, we need to find the largest probability value\n",
    "# to find out which digit the model predicts to be most likely in the image.\n",
    "prediction_digits = np.argmax(predictions, axis=1)\n",
    "\n",
    "# Then plot 100 random test images and their predicted labels.\n",
    "# If a prediction result is different from the label provided label in \"test\"\n",
    "# dataset, we will highlight it in red color.\n",
    "plt.figure(figsize=(18, 18))\n",
    "for i in range(100):\n",
    "  ax = plt.subplot(10, 10, i+1)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  plt.grid(False)\n",
    "  image_index = random.randint(0, len(prediction_digits))\n",
    "  plt.imshow(test_images[image_index], cmap=plt.cm.gray)\n",
    "  ax.xaxis.label.set_color(get_label_color(prediction_digits[image_index],\\\n",
    "                                           test_labels[image_index]))\n",
    "  plt.xlabel('Predicted: %d' % prediction_digits[image_index])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c0627f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\misty\\AppData\\Local\\Temp\\tmp9qawedsa\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\misty\\AppData\\Local\\Temp\\tmp9qawedsa\\assets\n"
     ]
    }
   ],
   "source": [
    "# Convert Keras model to TF Lite format.\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_float_model = converter.convert()\n",
    "\n",
    "# Show model size in KBs.\n",
    "float_model_size = len(tflite_float_model) / 1024\n",
    "print('Float model size = %dKBs.' % float_model_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dee724f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-convert the model to TF Lite using quantization.\n",
    "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
    "tflite_quantized_model = converter.convert()\n",
    "\n",
    "# Show model size in KBs.\n",
    "quantized_model_size = len(tflite_quantized_model) / 1024\n",
    "print('Quantized model size = %dKBs,' % quantized_model_size)\n",
    "print('which is about %d%% of the float model size.'\\\n",
    "      % (quantized_model_size * 100 / float_model_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc81b8dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A helper function to evaluate the TF Lite model using \"test\" dataset.\n",
    "def evaluate_tflite_model(tflite_model):\n",
    "  # Initialize TFLite interpreter using the model.\n",
    "  interpreter = tf.lite.Interpreter(model_content=tflite_model)\n",
    "  interpreter.allocate_tensors()\n",
    "  input_tensor_index = interpreter.get_input_details()[0][\"index\"]\n",
    "  output = interpreter.tensor(interpreter.get_output_details()[0][\"index\"])\n",
    "\n",
    "  # Run predictions on every image in the \"test\" dataset.\n",
    "  prediction_digits = []\n",
    "  for test_image in test_images:\n",
    "    # Pre-processing: add batch dimension and convert to float32 to match with\n",
    "    # the model's input data format.\n",
    "    test_image = np.expand_dims(test_image, axis=0).astype(np.float32)\n",
    "    interpreter.set_tensor(input_tensor_index, test_image)\n",
    "\n",
    "    # Run inference.\n",
    "    interpreter.invoke()\n",
    "\n",
    "    # Post-processing: remove batch dimension and find the digit with highest\n",
    "    # probability.\n",
    "    digit = np.argmax(output()[0])\n",
    "    prediction_digits.append(digit)\n",
    "\n",
    "  # Compare prediction results with ground truth labels to calculate accuracy.\n",
    "  accurate_count = 0\n",
    "  for index in range(len(prediction_digits)):\n",
    "    if prediction_digits[index] == test_labels[index]:\n",
    "      accurate_count += 1\n",
    "  accuracy = accurate_count * 1.0 / len(prediction_digits)\n",
    "\n",
    "  return accuracy\n",
    "\n",
    "# Evaluate the TF Lite float model. You'll find that its accurary is identical\n",
    "# to the original TF (Keras) model because they are essentially the same model\n",
    "# stored in different format.\n",
    "float_accuracy = evaluate_tflite_model(tflite_float_model)\n",
    "print('Float model accuracy = %.4f' % float_accuracy)\n",
    "\n",
    "# Evalualte the TF Lite quantized model.\n",
    "# Don't be surprised if you see quantized model accuracy is higher than\n",
    "# the original float model. It happens sometimes :)\n",
    "quantized_accuracy = evaluate_tflite_model(tflite_quantized_model)\n",
    "print('Quantized model accuracy = %.4f' % quantized_accuracy)\n",
    "print('Accuracy drop = %.4f' % (float_accuracy - quantized_accuracy))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a2fb190",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the quantized model to file to the Downloads directory\n",
    "f = open('mnist.tflite', \"wb\")\n",
    "f.write(tflite_quantized_model)\n",
    "f.close()\n",
    "\n",
    "# Download the digit classification model\n",
    "from google.colab import files\n",
    "files.download('mnist.tflite')\n",
    "\n",
    "print('`mnist.tflite` has been downloaded')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f11aa40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://colab.research.google.com/github/tensorflow/examples/blob/master/lite/codelabs/digit_classifier/ml/step2_train_ml_model.ipynb"
   ]
  }
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